Image analysis – Pattern recognition – Feature extraction
Reexamination Certificate
2001-02-21
2003-04-01
Chang, Jon (Department: 2623)
Image analysis
Pattern recognition
Feature extraction
C382S203000, C382S218000
Reexamination Certificate
active
06542638
ABSTRACT:
BACKGROUND OF THE INVENTION
This patent relates to the fields of visual pattern recognition, object recognition, image registration, and the matching of spatial patterns in two and higher dimensions.
References Cited
4,783,829
November 1988
Akira et al
382/199
4,891,762
January 1990
Chotiros
701/223
6,181,806
January 2001
Kado et al
382/100
S. E. Palmer, “Vision Science, Photons to Phenomenology”, MIT Press, Cambridge Mass., 1999.
S. Ullman, “High-level Vision: Object recognition and visual cognition”, MIT Press, Cambridge Mass., 1996.
H. Bunke and B. T. Messmer, “Recent advances in graph matching”, Int. J. Patt. Recog. Art. Intell., Vol. 11, No. 1, pp. 169-203, February 1997.
J. R. Ulmann, “An Algorithm for Subgraph Isomorphism”, J. Assoc. Comput. Mach., Vol. 23, No. 1, pp. 31-42, 1976.
L. Shapiro and R. M. Haralick, “A metric for comparing relational descriptions”, IEEE Patt. Anal. Mach. Int. Vol. 7, pp. 90-94, 1985.
J. E. Hummel and I. Biederman, “Dynamic binding in a neural network for shape recognition”, Psych. Rev., Vol. 99, pp. 480-517, 1992.
N. Ahuja, “Dot pattern processing using Voronoi Neighbourhoods”, IEEE Patt. Anal. Mach. Int. Vol. 4, pp. 336-343, 1982.
Y.-W. Chiang and R.-C. Wang, “Seal identification using the Delaunay tessellation”, Proc. Nat. Sci. Council, Rep. of China, Part A: Physical Sci. and Eng., Vol. 22, No. 6, pp. 751-757, November 1998.
G. Weber, L. Knipping, and H. Alt, “An application of point pattern matching in astronautics”, J. Symb. Comp., Vol. 17, No. 4, pp. 321-340, 1994.
M. Sambridge, J. Braun, and H. McQueen, “Geophysical parameterization and interpolation of irregular data using natural neighbors”, Geophysical Journal International Vol. 122, pp. 837-857, 1995.
Much work has been done in object recognition. S. E. Palmer's, “Vision Science, Photons to Phenomenology”, MIT Press, Cambridge Mass., 1999, and S. Ulman's “High-level Vision: Object recognition and visual cognition”, MIT Press, Cambridge Mass., 1996, are works that contain more specific references to some of the basic topics on pattern recognition mentioned in this section.
Some useful pattern recognition algorithms are based on template matching. A basic description of template matching is as follows. A ‘square’ template is a binary array: the edges of the square are given a value of ‘1’ and the background and central portion of the square are given a value of ‘0’. The edges of an input object are found and compared to the ‘square’ template. If the position, shape, orientation, and scale of the input object are very near to that of the ‘square’ template, then a correlation of the input object with the ‘square’ template yields a high value indicating a match. If the input object is a square, but is at a slightly different position, orientation, or scale, template matching fails. How to match identical patterns that are transformed in position, rotation, and scale is a currently unsolved problem in the field of pattern recognition.
Other object recognition algorithms have been based on analysis of the Fourier spectra of objects. These algorithms are rotation invariant, but are not scale invariant. A specific problem with this approach is that often the entire image is used and objects are not segmented prior to analysis. Thus information about multiple objects is sometimes included in the Fourier spectra. Furthermore, if half of the object is occluded, this method of shape matching fails because large scale features, or low frequency components, will be different from those stored in the matching template.
One can also approximate the shape of an object using Fourier components. Fourier components are able to represent single closed contours efficiently using the coefficients of periodic functions. A measure for the distance between the two vectors containing those coefficients is used to determine a match. This approach is rotation invariant, but not scale invariant. Furthermore, it does not represent complex two dimensional objects readily.
Another approach is structural descriptions. Structural descriptions are interesting because they have the potential to perform position, rotation, and scale invariant pattern recognition. Structural descriptions consist of graphs in which the nodes indicate some feature of the object and the connections between nodes indicate spatial relationships such as up, down, right, left, top, bottom, and middle. For example, S. E. Palmer, in his book “Vision Science, Photons to Phenomenology”, MIT Press, Cambridge Mass., 1999, pp. 394, presents a structural graph for the letter ‘A’. The graph for this object contains 12 nodes and 14 edges. This is a surprisingly complex graph for such a simple object. The relationship between the complexity of the object and the size of the resulting graph is unknown. Furthermore, one can construct different graphs for this object. To date, there is no consistent and automatic method of generating these structural graphs.
Another complication of structural graphs is that matching graphs is itself a complex problem. For examples of various approaches to the graph matching problem in the computer vision domain, see H. Bunke and B. T. Messmer, “Recent advances in graph matching”, Int. J. Patt. Recog. Art. Intell., Vol. 11, No. 1, pp. 169-203, February 1997, J. R. Ullmann “An Algorithm for Subgraph Isomorphism” J. Assoc. Comput. Mach., Vol. 23, No. 1, pp. 31-42, 1976, and L. Shapiro and R. M. Haralick, “A metric for comparing relational descriptions”, IEEE Patt. Anal. Mach. Int. vol. 7, pp. 90-94, 1985. All of these works contain algorithms that are complex and time consuming for objects with more than a few edges and nodes.
Other researchers have tried to encode spatial relationships with neural networks. J. E. Hummel and I. Biederman, “Dynamic binding in a neural network for shape recognition”, Psych. Rev., Vol. 99, pp. 480-517, 1992, use neural networks to encode spatial relationships between geons in the RBC (recognition by components) theory of object recognition. These neural networks encode features such as location, orientation, and scale. While this system works for simple objects, it is unclear how the system performs on multiple objects that contain many features. This is because the system has limited capability in representing different locations, orientations, and scale. Therefore the system may fail when making fine distinctions between objects with large numbers of features.
Several other researchers use a rigid mathematical procedure (e.g. Voronoi tessellation, Delaunay triangulation, Gabriel graphs, and minimal spanning trees) to create graphs from feature points and then perform graph matching. N. Ahija, in his paper, “Dot pattern processing using Voronoi Neighbourhoods”, IEEE Patt. Anal. Mach. Int. Vol. 4, pp. 336-343, 1982, suggests a relaxation labeling method to perform matching of dot patterns using Voronoi tessellation, a method both complex and time consuming for large objects. In Y.-W. Chiang and R.-C. Wang, “Seal identification using the Delaunay tessellation”, Proc. Nat. Sci. Council, Rep. of China, Part A: Physical Sci. and Eng., Vol. 22, No. 6, pp. 751-757, November 1998, the authors match histograms of the areas of resulting Delaunay triangles to recognize Chinese Seals. In G. Weber, L. Knipping, and H. Alt, “An application of point pattern matching in astronautics”, J. Symb. Comp., Vol. 17, No. 4, pp. 321-340, 1994, the authors use a Delaunay triangulation between stars and then look for matches in the slope and length of edges between pairs of stars, thus their algorithm is not rotation or scale invariant. A similar matching method is proposed in N. P. Chotiros' U.S. Pat. No. 4,891,762 entitled “Method and apparatus for tracking, mapping and recognition of spatial patterns”. Chotiros' algorithm matches only lengths of edges in a Delaunay triangulation and thus achieves invariant matching with respect to rotation, but not with respect to scale.
In U.S. Pat. No. 6,181,806 issued to Kado et al, entitled “Apparatus for identifying a person using facial features”, a method is
Chang Jon
Gene Scott-Patent Law & Venture Group
LandOfFree
Method for matching spatial patterns does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method for matching spatial patterns, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for matching spatial patterns will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3035713